Great environmental problems are facing us at an unprecedented level.One way of approaching these global challenges is by transitioning from a linear economy to a circular one. In a circular economy, product and material flows become circular, which can significantly improve resource efficiency for environmental sustainability. This can help with minimizing waste and pollution and aid in the regeneration of nature.Meanwhile, transitioning from linear business models to circular business models (CBMs) often leads to a number of financial risks for product companies, since they need to secure more capital in a stock of products that will be rented out over time. This leads to a slower, more volatile cash flow in the short term compared to linear direct sales of products.In this work, we address this problem by reducing the uncertainty of the future value of products. This can increase the willingness among financiers to be part of the development of new circular business models (CBMs). In particular, we study the predictability of online auction end prices using machine learning. The models are trained and evaluated on data collected from a Swedish online auction site.Our results show that deep learning is able to model the residual value of second-hand items on the market using user-uploaded text and images. Our hypothesis is that this technique will be useful to estimate the value of second-hand inventories and to help estimate the value of circular businesses, aiding in a transition from a linear to a circular economy.